With an increase in deep learning-based methods, the call for explainability of such methods
grows, especially in high-stakes decision making areas such as medical image analysis …

Over the last decade, deep learning (DL) has contributed to a paradigm shift in computer
vision and image recognition creating widespread opportunities of using artificial …

Medical images differ from natural images in significantly higher resolutions and smaller
regions of interest. Because of these differences, neural network architectures that work well …

Training robust deep learning (DL) systems for medical image classification or segmentation
is challenging due to limited images covering different disease types and severity. We …

In supervised learning for medical image analysis, sample selection methodologies are
fundamental to attain optimum system performance promptly and with minimal expert …

Using deep learning models to diagnose cancer from histology data presents several
challenges. Cancer grading and localization of regions of interest (ROIs) in these images …

Although generative adversarial network (GAN) based style transfer is state of the art in
histopathology color-stain normalization, they do not explicitly integrate structural …

Pulmonary tuberculosis (PTB) is a bacterial infection that affects the lung. PTB remains one
of the infectious diseases with the highest global mortalities. Chest radiography is a …

Coronavirus disease 2019 (COVID-19) is one of the most destructive pandemic after
millennium, forcing the world to tackle a health crisis. Automated lung infections …

Automatic chest X-ray (CXR) disease classification has drawn increasing public attention as
CXR is widely used in thoracic disease diagnosis. Existing classification networks typically …